Bottom Line:
A genetic algorithm coupled with multivariate classification identified a small number of spectral components that showed clear discrimination between LIU and CU samples with sensitivity and specificity >90%.Assignment of specific resonances indicated that some metabolites involved in the arginase pathway were significantly more abundant in LIU than CU.Collectively the data demonstrates the efficacy of metabolomic analysis to distinguish between ocular inflammatory diseases.

Affiliation:
Department of Rheumatology, University of Birmingham, Birmingham, UK.

ABSTRACT

Purpose: Vitreoretinal disorders lack specific biomarkers that define either disease type or response to treatment. We have used NMR-based metabolomic analysis of human vitreous humor to assess the applicability of this approach to the study of ocular disease.

Methods: Vitreous samples from patients with a range of vitreoretinal disorders were subjected to high-resolution (1)H-nuclear magnetic resonance spectroscopy (NMR). Good quality spectra were derived from the vitreous samples, and the profiles were analyzed by three different methods.

Results: Principal component analysis (PCA) showed a wide dispersal of the different clinical conditions. Partial least squares discriminant analysis (PLS-DA) was used to define differences between lens-induced uveitis (LIU) and chronic uveitis (CU) and could distinguish between these conditions with a sensitivity of 78% and specificity of 85%. A genetic algorithm coupled with multivariate classification identified a small number of spectral components that showed clear discrimination between LIU and CU samples with sensitivity and specificity >90%. Assignment of specific resonances indicated that some metabolites involved in the arginase pathway were significantly more abundant in LIU than CU.

Conclusion: The discrimination we observed based on PCA, PLS-DA, and multivariate variable selection analysis of the NMR spectra suggests that a complex mix of metabolites are present in vitreous fluid of different uveitic conditions as a result of the disease process. Collectively the data demonstrates the efficacy of metabolomic analysis to distinguish between ocular inflammatory diseases.

f3: Partial least squares discriminant analysis (PLS-DA) of vitreous fluid NMR spectra from lens-induced uveitis and chronic uveitis patients. A PLS-DA model was constructed to predict diagnostic groups. A: Latent variable plot of the PLS-DA model, showing improved discrimination between LIU and chronic uveitis compared with the PCA analysis (Figure 2). B: Prediction of the group membership from the PLSDA. The model was cross validated, and was able to predict the classes, LIU and chronic uveitis, with sensitivity of 78% and specificity of 85%.

Mentions:
The supervised clustering analysis Partial Least Squares Discriminant Analysis (PLS-DA) was carried out to enhance the separation seen with the PCA. The PLS-DA model was cross-validated using Venetian blinds [21,22], a method which re-assigns randomly selected blocks of data to the PLS-DA model to determine the accuracy of the model in correctly assigning class membership. Using this approach an improved separation was seen between LIU and CU in the plot of Latent Variables (Figure 3A). PLS-DA allows a predictive model to be generated and this was able to accurately predict which class a sample came from (Figure 3B) with a sensitivity of 78% and specificity of 85%.

f3: Partial least squares discriminant analysis (PLS-DA) of vitreous fluid NMR spectra from lens-induced uveitis and chronic uveitis patients. A PLS-DA model was constructed to predict diagnostic groups. A: Latent variable plot of the PLS-DA model, showing improved discrimination between LIU and chronic uveitis compared with the PCA analysis (Figure 2). B: Prediction of the group membership from the PLSDA. The model was cross validated, and was able to predict the classes, LIU and chronic uveitis, with sensitivity of 78% and specificity of 85%.

Mentions:
The supervised clustering analysis Partial Least Squares Discriminant Analysis (PLS-DA) was carried out to enhance the separation seen with the PCA. The PLS-DA model was cross-validated using Venetian blinds [21,22], a method which re-assigns randomly selected blocks of data to the PLS-DA model to determine the accuracy of the model in correctly assigning class membership. Using this approach an improved separation was seen between LIU and CU in the plot of Latent Variables (Figure 3A). PLS-DA allows a predictive model to be generated and this was able to accurately predict which class a sample came from (Figure 3B) with a sensitivity of 78% and specificity of 85%.

Bottom Line:
A genetic algorithm coupled with multivariate classification identified a small number of spectral components that showed clear discrimination between LIU and CU samples with sensitivity and specificity >90%.Assignment of specific resonances indicated that some metabolites involved in the arginase pathway were significantly more abundant in LIU than CU.Collectively the data demonstrates the efficacy of metabolomic analysis to distinguish between ocular inflammatory diseases.

Affiliation:
Department of Rheumatology, University of Birmingham, Birmingham, UK.

ABSTRACT

Purpose: Vitreoretinal disorders lack specific biomarkers that define either disease type or response to treatment. We have used NMR-based metabolomic analysis of human vitreous humor to assess the applicability of this approach to the study of ocular disease.

Methods: Vitreous samples from patients with a range of vitreoretinal disorders were subjected to high-resolution (1)H-nuclear magnetic resonance spectroscopy (NMR). Good quality spectra were derived from the vitreous samples, and the profiles were analyzed by three different methods.

Results: Principal component analysis (PCA) showed a wide dispersal of the different clinical conditions. Partial least squares discriminant analysis (PLS-DA) was used to define differences between lens-induced uveitis (LIU) and chronic uveitis (CU) and could distinguish between these conditions with a sensitivity of 78% and specificity of 85%. A genetic algorithm coupled with multivariate classification identified a small number of spectral components that showed clear discrimination between LIU and CU samples with sensitivity and specificity >90%. Assignment of specific resonances indicated that some metabolites involved in the arginase pathway were significantly more abundant in LIU than CU.

Conclusion: The discrimination we observed based on PCA, PLS-DA, and multivariate variable selection analysis of the NMR spectra suggests that a complex mix of metabolites are present in vitreous fluid of different uveitic conditions as a result of the disease process. Collectively the data demonstrates the efficacy of metabolomic analysis to distinguish between ocular inflammatory diseases.